code: def test_standard_classification_datasets(dataset):
      from sklearn.datasets import make_classification
  
      X, y = dataset
      assert X.ndim == 2
      assert X.shape[0] <= 200
      assert X.shape[1] <= 200
      assert (y.ndim == 1 and y.shape[0] <= 200) or y.ndim == 0
      assert np.issubdtype(X.dtype, np.floating)
      assert np.issubdtype(y.dtype, np.integer)
  
      X_cmp, y_cmp = make_classification(
          n_samples=X.shape[0],
          n_features=X.shape[1],
          random_state=0,
      )
  
      assert X.dtype.type == X_cmp.dtype.type
      assert X.ndim == X_cmp.ndim
      assert X.shape == X_cmp.shape
      assert y.dtype.type == y_cmp.dtype.type
      assert y.ndim == y_cmp.ndim
      assert y.shape == y_cmp.shape
      assert (X == X_cmp).all()
      assert (y == y_cmp).all()
  
file_path: cuml/python/cuml/tests/test_strategies.py
imports: from cuml.internals.array import CumlArray
  from cuml.internals.safe_imports import cpu_only_import, gpu_only_import
  from cuml.testing.strategies import create_cuml_array_input, cuml_array_dtypes, cuml_array_input_types, cuml_array_inputs, cuml_array_orders, cuml_array_shapes, regression_datasets, split_datasets, standard_classification_datasets, standard_datasets, standard_regression_datasets
  from cuml.testing.utils import normalized_shape, series_squeezed_shape
  from hypothesis import HealthCheck, given, settings
  from hypothesis import strategies
  from hypothesis.extra.numpy import floating_dtypes, integer_dtypes
label_desc: Use image node_id 11 for calling a global function with example usage: test_standard_classification_datasets(dataset) without return types